Evaluation and comparison of the performances of the CMIP5 and CMIP6 models in reproducing extreme rainfall in the Upper Blue Nile basin of Ethiopia

Abstract

Understanding the characteristics of extreme rainfall is vital for planning effective adaptation and mitigation measures. Thus, this study aims to evaluate the performances of 16 general circulation models (GCMs) of the coupled model intercomparison project (8 CMIP5 and 8 CMIP6) in reproducing observed extreme rainfall indices (ERFIs) and monthly rainfall in the Upper Blue Nile (UBN) basin of Ethiopia (1981–2005). The observed ERFIs were computed based on rainfall estimates of the random forest merging (RF-MERGE) algorithm, which combines ground-based rainfall with three gridded rainfall products (GRFPs). The GCMs were evaluated using statistical performance measures such as the Pearson correlation coefficient (R), root mean square error (RMSE), and percent bias (PBIAS). Using the Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS), the GCMs were ranked from least skilled to most skilled. Accordingly, MIROC5 of the CMIP5 and MPI-ESM1-2-LR of the CMIP6 models were found to be the most suitable models. Furthermore, the top-ranked models were selected and bias corrected using quantile mapping (QM), and their ensembles (CMIP5-ensemble and CMIP6-ensemble) were used for projecting future extremes under representative concentration pathways (RCP4.5 and RCP8.5) and shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5), respectively, for the periods 2031–2055 and 2056–2080. Most of the ERFIs exhibited high variability and inconsistent trends for both the observations and future periods. The findings of this study provide valuable insights the the impacts of climate change on ERFIs, and the developed framework can serves as a useful reference for future research.